Abstract
The performance of an automatic fingerprint authentication system relies heavily on the quality of the captured fingerprint images. In this paper, two new quality indices for fingerprint images are developed. The first index measures the energy concentration in the frequency domain as a global feature. The second index measures the spatial coherence in local regions. We present a novel framework for evaluating and comparing quality indices in terms of their capability of predicting the system performance at three different stages, namely, image enhancement, feature extraction and matching. Experimental results on the IBM-HURSLEY and FVC2002 DB3 databases demonstrate that the global index is better than the local index in the enhancement stage (correlation of 0.70 vs. 0.50) and comparative in the feature extraction stage (correlation of 0.70 vs. 0.71). Both quality indices are effective in predicting the matching performance, and by applying a quality-based weighting scheme in the matching algorithm, the overall matching performance can be improved; a decrease of 1.94% in EER is observed on the FVC2002 DB3 database.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Tabassi, E., Wilson, C., Watson, C.: Fingerprint Image Quality. NIST research report NISTIR7151 (August 2004)
Bolle, R., et al.: System and methods for determing the quality of fingerprint images. United Sates patent number US596356 (1999)
Hong, L., Wan, Y., Jain, A.: Fingerprint image enhancement: algorithms and performance evaluation. IEEE Transactions on Pattern Analysis and Machine Intelligence 20, 777–789 (1998)
Shen, L., Kot, A., Koo, W.: Quality measures of fingerprint images. In: Audio- and Video-based Biometric Person Authentication (2001)
Ratha, N., Bolle, R.: Fingerprint image quality estimation. IBM computer science research report RC21622 (1999)
Lim, E., Jiang, X., Yau, W.: Fingerprint quality and validity analysis. IEEE International Conference on Image Processing 1, 469–472 (2002)
Rosenfeld, A., Kak, A.: Digital Picture Processing. Academic Press, London (1982)
Hong, L., Jain, A., Pankanti, S., Bolle, R.: Fingerprint Enhancement. In: IEEE Workshop on Applications of Computer Vision, pp. 202–207 (1996)
Hong, L., Wan, Y., Jain, A.: Fingerprint Image Enhancement: Algorithms and Performance Evaluation. IEEE Transactions on PAMI 20(8), 777–789 (1998)
Jain, A., Hong, L., Bolle, R.: On-line fingerprint verification. IEEE Trasactions on Pattern Analysis and Machine Intelligence 19(4), 302–314 (1997)
Maltoni, D., Cappelli, R., Wayman, J., Jain, A.: FVC 2002: Second Fingerprint Verification Competition. In: International Conference on Pattern Recognition, vol. 3, pp. 811–814 (2002)
Jain, A., Prabhakar, S., Chen, S.: Combining Multiple Matchers for a High Security Fingerprint Verification System. Pattern Recognition Letters 20, 1371–1379 (1999)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2005 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Chen, Y., Dass, S.C., Jain, A.K. (2005). Fingerprint Quality Indices for Predicting Authentication Performance. In: Kanade, T., Jain, A., Ratha, N.K. (eds) Audio- and Video-Based Biometric Person Authentication. AVBPA 2005. Lecture Notes in Computer Science, vol 3546. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11527923_17
Download citation
DOI: https://doi.org/10.1007/11527923_17
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-27887-0
Online ISBN: 978-3-540-31638-1
eBook Packages: Computer ScienceComputer Science (R0)